Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations9578
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory175.1 B

Variable types

Categorical3
Numeric11

Alerts

credit.policy is highly overall correlated with inq.last.6mthsHigh correlation
fico is highly overall correlated with int.rate and 1 other fieldsHigh correlation
inq.last.6mths is highly overall correlated with credit.policyHigh correlation
int.rate is highly overall correlated with ficoHigh correlation
revol.bal is highly overall correlated with revol.utilHigh correlation
revol.util is highly overall correlated with fico and 1 other fieldsHigh correlation
revol.bal has 321 (3.4%) zerosZeros
revol.util has 297 (3.1%) zerosZeros
inq.last.6mths has 3637 (38.0%) zerosZeros
delinq.2yrs has 8458 (88.3%) zerosZeros
pub.rec has 9019 (94.2%) zerosZeros

Reproduction

Analysis started2025-10-07 00:25:34.096350
Analysis finished2025-10-07 00:25:46.826431
Duration12.73 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

credit.policy
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size542.6 KiB
1
7710 
0
1868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9578
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Length

2025-10-07T05:55:46.890171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-07T05:55:46.961143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring characters

ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)9578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

purpose
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size664.8 KiB
debt_consolidation
3957 
all_other
2331 
credit_card
1262 
home_improvement
629 
small_business
619 
Other values (2)
780 

Length

Max length18
Median length16
Mean length14.064314
Min length9

Characters and Unicode

Total characters134708
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowcredit_card
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowcredit_card

Common Values

ValueCountFrequency (%)
debt_consolidation3957
41.3%
all_other2331
24.3%
credit_card1262
 
13.2%
home_improvement629
 
6.6%
small_business619
 
6.5%
major_purchase437
 
4.6%
educational343
 
3.6%

Length

2025-10-07T05:55:47.036060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-07T05:55:47.132112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
debt_consolidation3957
41.3%
all_other2331
24.3%
credit_card1262
 
13.2%
home_improvement629
 
6.6%
small_business619
 
6.5%
major_purchase437
 
4.6%
educational343
 
3.6%

Most occurring characters

ValueCountFrequency (%)
o16240
12.1%
t12479
9.3%
e10836
 
8.0%
d10781
 
8.0%
i10767
 
8.0%
l10200
 
7.6%
a9729
 
7.2%
n9505
 
7.1%
_9235
 
6.9%
c7261
 
5.4%
Other values (9)27675
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)134708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o16240
12.1%
t12479
9.3%
e10836
 
8.0%
d10781
 
8.0%
i10767
 
8.0%
l10200
 
7.6%
a9729
 
7.2%
n9505
 
7.1%
_9235
 
6.9%
c7261
 
5.4%
Other values (9)27675
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)134708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o16240
12.1%
t12479
9.3%
e10836
 
8.0%
d10781
 
8.0%
i10767
 
8.0%
l10200
 
7.6%
a9729
 
7.2%
n9505
 
7.1%
_9235
 
6.9%
c7261
 
5.4%
Other values (9)27675
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)134708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o16240
12.1%
t12479
9.3%
e10836
 
8.0%
d10781
 
8.0%
i10767
 
8.0%
l10200
 
7.6%
a9729
 
7.2%
n9505
 
7.1%
_9235
 
6.9%
c7261
 
5.4%
Other values (9)27675
20.5%

int.rate
Real number (ℝ)

High correlation 

Distinct249
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12264006
Minimum0.06
Maximum0.2164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:47.243031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.0774
Q10.1039
median0.1221
Q30.1407
95-th percentile0.167
Maximum0.2164
Range0.1564
Interquartile range (IQR)0.0368

Descriptive statistics

Standard deviation0.026846987
Coefficient of variation (CV)0.21890879
Kurtosis-0.22432351
Mean0.12264006
Median Absolute Deviation (MAD)0.0186
Skewness0.16441991
Sum1174.6465
Variance0.00072076072
MonotonicityNot monotonic
2025-10-07T05:55:47.351036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1253354
 
3.7%
0.0894299
 
3.1%
0.1183243
 
2.5%
0.1218215
 
2.2%
0.0963210
 
2.2%
0.1114206
 
2.2%
0.08198
 
2.1%
0.1287197
 
2.1%
0.1148193
 
2.0%
0.0859187
 
2.0%
Other values (239)7276
76.0%
ValueCountFrequency (%)
0.068
 
0.1%
0.06394
 
< 0.1%
0.06769
 
0.1%
0.070523
 
0.2%
0.07129
 
0.1%
0.071428
 
0.3%
0.073732
0.3%
0.07472
0.8%
0.074333
0.3%
0.075138
0.4%
ValueCountFrequency (%)
0.21642
 
< 0.1%
0.21217
0.1%
0.2092
 
< 0.1%
0.20866
0.1%
0.20524
< 0.1%
0.20176
0.1%
0.20161
 
< 0.1%
0.20119
0.1%
0.19828
0.1%
0.19796
0.1%

installment
Real number (ℝ)

Distinct4788
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.08941
Minimum15.67
Maximum940.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:47.489448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15.67
5-th percentile65.5595
Q1163.77
median268.95
Q3432.7625
95-th percentile756.2655
Maximum940.14
Range924.47
Interquartile range (IQR)268.9925

Descriptive statistics

Standard deviation207.0713
Coefficient of variation (CV)0.64894444
Kurtosis0.13790774
Mean319.08941
Median Absolute Deviation (MAD)124.7
Skewness0.91252246
Sum3056238.4
Variance42878.524
MonotonicityNot monotonic
2025-10-07T05:55:47.602157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317.7241
 
0.4%
316.1134
 
0.4%
319.4729
 
0.3%
381.2627
 
0.3%
662.6827
 
0.3%
156.124
 
0.3%
320.9524
 
0.3%
669.3323
 
0.2%
334.6723
 
0.2%
188.0223
 
0.2%
Other values (4778)9303
97.1%
ValueCountFrequency (%)
15.671
< 0.1%
15.691
< 0.1%
15.751
< 0.1%
15.761
< 0.1%
15.911
< 0.1%
16.081
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
16.471
< 0.1%
16.731
< 0.1%
ValueCountFrequency (%)
940.141
 
< 0.1%
926.832
< 0.1%
922.421
 
< 0.1%
918.022
< 0.1%
916.952
< 0.1%
914.422
< 0.1%
913.633
< 0.1%
910.441
 
< 0.1%
909.251
 
< 0.1%
907.62
< 0.1%

log.annual.inc
Real number (ℝ)

Distinct1987
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.932117
Minimum7.5475017
Maximum14.528354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:47.723655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.5475017
5-th percentile9.9178933
Q110.558414
median10.928884
Q311.291293
95-th percentile11.918391
Maximum14.528354
Range6.9808528
Interquartile range (IQR)0.7328794

Descriptive statistics

Standard deviation0.61481275
Coefficient of variation (CV)0.056239129
Kurtosis1.6090041
Mean10.932117
Median Absolute Deviation (MAD)0.36694576
Skewness0.028668107
Sum104707.82
Variance0.37799472
MonotonicityNot monotonic
2025-10-07T05:55:47.848682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.00209984308
 
3.2%
10.81977828248
 
2.6%
10.59663473224
 
2.3%
10.30895266224
 
2.3%
10.71441777221
 
2.3%
11.22524339196
 
2.0%
11.15625052165
 
1.7%
10.77895629149
 
1.6%
10.91508846147
 
1.5%
11.08214255146
 
1.5%
Other values (1977)7550
78.8%
ValueCountFrequency (%)
7.5475016831
 
< 0.1%
7.600902461
 
< 0.1%
8.1016777471
 
< 0.1%
8.1605182471
 
< 0.1%
8.1886891241
 
< 0.1%
8.294049643
< 0.1%
8.3428398041
 
< 0.1%
8.4118326761
 
< 0.1%
8.4763711972
< 0.1%
8.4945385011
 
< 0.1%
ValueCountFrequency (%)
14.528354481
 
< 0.1%
14.180153671
 
< 0.1%
14.124464771
 
< 0.1%
13.997832111
 
< 0.1%
13.710150042
< 0.1%
13.56704922
< 0.1%
13.543701831
 
< 0.1%
13.487006491
 
< 0.1%
13.470199371
 
< 0.1%
13.458835613
< 0.1%

dti
Real number (ℝ)

Distinct2529
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.606679
Minimum0
Maximum29.96
Zeros89
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:47.974925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.27
Q17.2125
median12.665
Q317.95
95-th percentile23.65
Maximum29.96
Range29.96
Interquartile range (IQR)10.7375

Descriptive statistics

Standard deviation6.8839695
Coefficient of variation (CV)0.54605734
Kurtosis-0.90035536
Mean12.606679
Median Absolute Deviation (MAD)5.385
Skewness0.023941023
Sum120746.77
Variance47.389037
MonotonicityNot monotonic
2025-10-07T05:55:48.090740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
089
 
0.9%
1019
 
0.2%
0.616
 
0.2%
19.213
 
0.1%
1213
 
0.1%
613
 
0.1%
13.1613
 
0.1%
15.113
 
0.1%
15.612
 
0.1%
10.812
 
0.1%
Other values (2519)9365
97.8%
ValueCountFrequency (%)
089
0.9%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.042
 
< 0.1%
0.051
 
< 0.1%
0.073
 
< 0.1%
0.082
 
< 0.1%
0.092
 
< 0.1%
0.122
 
< 0.1%
ValueCountFrequency (%)
29.961
< 0.1%
29.951
< 0.1%
29.91
< 0.1%
29.741
< 0.1%
29.721
< 0.1%
29.72
< 0.1%
29.61
< 0.1%
29.471
< 0.1%
29.421
< 0.1%
29.411
< 0.1%

fico
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.84631
Minimum612
Maximum827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:48.205723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum612
5-th percentile657
Q1682
median707
Q3737
95-th percentile782
Maximum827
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation37.970537
Coefficient of variation (CV)0.053415958
Kurtosis-0.42231231
Mean710.84631
Median Absolute Deviation (MAD)25
Skewness0.47125974
Sum6808486
Variance1441.7617
MonotonicityNot monotonic
2025-10-07T05:55:48.317526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
687548
 
5.7%
682536
 
5.6%
692498
 
5.2%
697476
 
5.0%
702472
 
4.9%
707444
 
4.6%
667438
 
4.6%
677427
 
4.5%
717424
 
4.4%
662414
 
4.3%
Other values (34)4901
51.2%
ValueCountFrequency (%)
6122
 
< 0.1%
6171
 
< 0.1%
6221
 
< 0.1%
6272
 
< 0.1%
6326
 
0.1%
6375
 
0.1%
642102
1.1%
647112
1.2%
652131
1.4%
657127
1.3%
ValueCountFrequency (%)
8271
 
< 0.1%
8225
 
0.1%
8176
 
0.1%
81233
 
0.3%
80745
 
0.5%
80255
0.6%
79776
0.8%
79297
1.0%
78785
0.9%
782118
1.2%

days.with.cr.line
Real number (ℝ)

Distinct2687
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4560.7672
Minimum178.95833
Maximum17639.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:48.436809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum178.95833
5-th percentile1320.0417
Q12820
median4139.9583
Q35730
95-th percentile9329.9583
Maximum17639.958
Range17461
Interquartile range (IQR)2910

Descriptive statistics

Standard deviation2496.9304
Coefficient of variation (CV)0.54748034
Kurtosis1.9378606
Mean4560.7672
Median Absolute Deviation (MAD)1440.0833
Skewness1.1557482
Sum43683028
Variance6234661.3
MonotonicityNot monotonic
2025-10-07T05:55:48.564219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
366050
 
0.5%
363048
 
0.5%
399046
 
0.5%
441044
 
0.5%
360041
 
0.4%
408038
 
0.4%
255038
 
0.4%
180037
 
0.4%
369037
 
0.4%
402035
 
0.4%
Other values (2677)9164
95.7%
ValueCountFrequency (%)
178.95833331
 
< 0.1%
180.04166673
< 0.1%
1811
 
< 0.1%
183.04166671
 
< 0.1%
209.04166671
 
< 0.1%
2101
 
< 0.1%
212.04166671
 
< 0.1%
238.95833335
0.1%
240.04166671
 
< 0.1%
291.95833331
 
< 0.1%
ValueCountFrequency (%)
17639.958331
< 0.1%
176161
< 0.1%
166521
< 0.1%
163501
< 0.1%
162601
< 0.1%
16259.041671
< 0.1%
162131
< 0.1%
159901
< 0.1%
156921
< 0.1%
15420.958331
< 0.1%

revol.bal
Real number (ℝ)

High correlation  Zeros 

Distinct7869
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16913.964
Minimum0
Maximum1207359
Zeros321
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:48.681990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile127.7
Q13187
median8596
Q318249.5
95-th percentile57654.3
Maximum1207359
Range1207359
Interquartile range (IQR)15062.5

Descriptive statistics

Standard deviation33756.19
Coefficient of variation (CV)1.9957586
Kurtosis259.6552
Mean16913.964
Median Absolute Deviation (MAD)6488
Skewness11.161058
Sum1.6200195 × 108
Variance1.1394803 × 109
MonotonicityNot monotonic
2025-10-07T05:55:48.815583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0321
 
3.4%
25510
 
0.1%
29810
 
0.1%
6829
 
0.1%
3468
 
0.1%
22296
 
0.1%
10856
 
0.1%
1826
 
0.1%
80355
 
0.1%
15
 
0.1%
Other values (7859)9192
96.0%
ValueCountFrequency (%)
0321
3.4%
15
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
42
 
< 0.1%
54
 
< 0.1%
65
 
0.1%
71
 
< 0.1%
94
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
12073591
< 0.1%
9520131
< 0.1%
6025191
< 0.1%
5089611
< 0.1%
4077941
< 0.1%
4019411
< 0.1%
3941071
< 0.1%
3888921
< 0.1%
3854891
< 0.1%
3744871
< 0.1%

revol.util
Real number (ℝ)

High correlation  Zeros 

Distinct1035
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.799236
Minimum0
Maximum119
Zeros297
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:48.927875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q122.6
median46.3
Q370.9
95-th percentile94
Maximum119
Range119
Interquartile range (IQR)48.3

Descriptive statistics

Standard deviation29.014417
Coefficient of variation (CV)0.6199763
Kurtosis-1.116467
Mean46.799236
Median Absolute Deviation (MAD)24.2
Skewness0.059985443
Sum448243.08
Variance841.83639
MonotonicityNot monotonic
2025-10-07T05:55:49.065663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0297
 
3.1%
0.526
 
0.3%
47.822
 
0.2%
0.322
 
0.2%
73.722
 
0.2%
0.121
 
0.2%
3.321
 
0.2%
0.720
 
0.2%
120
 
0.2%
0.220
 
0.2%
Other values (1025)9087
94.9%
ValueCountFrequency (%)
0297
3.1%
0.041
 
< 0.1%
0.121
 
0.2%
0.220
 
0.2%
0.322
 
0.2%
0.412
 
0.1%
0.526
 
0.3%
0.612
 
0.1%
0.720
 
0.2%
0.814
 
0.1%
ValueCountFrequency (%)
1191
< 0.1%
108.81
< 0.1%
106.51
< 0.1%
106.41
< 0.1%
106.21
< 0.1%
106.11
< 0.1%
105.71
< 0.1%
105.31
< 0.1%
105.21
< 0.1%
104.31
< 0.1%

inq.last.6mths
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5774692
Minimum0
Maximum33
Zeros3637
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:49.503957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2002453
Coefficient of variation (CV)1.3947945
Kurtosis26.288131
Mean1.5774692
Median Absolute Deviation (MAD)1
Skewness3.5841509
Sum15109
Variance4.8410794
MonotonicityNot monotonic
2025-10-07T05:55:49.653217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
03637
38.0%
12462
25.7%
21384
 
14.4%
3864
 
9.0%
4475
 
5.0%
5278
 
2.9%
6165
 
1.7%
7100
 
1.0%
872
 
0.8%
947
 
0.5%
Other values (18)94
 
1.0%
ValueCountFrequency (%)
03637
38.0%
12462
25.7%
21384
 
14.4%
3864
 
9.0%
4475
 
5.0%
5278
 
2.9%
6165
 
1.7%
7100
 
1.0%
872
 
0.8%
947
 
0.5%
ValueCountFrequency (%)
331
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
251
 
< 0.1%
242
< 0.1%
201
 
< 0.1%
192
< 0.1%
184
< 0.1%

delinq.2yrs
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1637085
Minimum0
Maximum13
Zeros8458
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:49.819216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54621492
Coefficient of variation (CV)3.3365093
Kurtosis71.433182
Mean0.1637085
Median Absolute Deviation (MAD)0
Skewness6.0617933
Sum1568
Variance0.29835074
MonotonicityNot monotonic
2025-10-07T05:55:49.915383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
08458
88.3%
1832
 
8.7%
2192
 
2.0%
365
 
0.7%
419
 
0.2%
56
 
0.1%
62
 
< 0.1%
131
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
08458
88.3%
1832
 
8.7%
2192
 
2.0%
365
 
0.7%
419
 
0.2%
56
 
0.1%
62
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
111
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
62
 
< 0.1%
56
 
0.1%
419
 
0.2%
365
 
0.7%
2192
 
2.0%
1832
8.7%

pub.rec
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062121529
Minimum0
Maximum5
Zeros9019
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2025-10-07T05:55:50.004333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26212633
Coefficient of variation (CV)4.219573
Kurtosis38.781007
Mean0.062121529
Median Absolute Deviation (MAD)0
Skewness5.1264345
Sum595
Variance0.068710211
MonotonicityNot monotonic
2025-10-07T05:55:50.099690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
09019
94.2%
1533
 
5.6%
219
 
0.2%
35
 
0.1%
41
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
09019
94.2%
1533
 
5.6%
219
 
0.2%
35
 
0.1%
41
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
219
 
0.2%
1533
 
5.6%
09019
94.2%

not.fully.paid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size542.6 KiB
0
8045 
1
1533 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9578
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Length

2025-10-07T05:55:50.187376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-07T05:55:50.257100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring characters

ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)9578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Interactions

2025-10-07T05:55:45.440915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:34.580433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.669751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.627470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.090140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.919577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.849279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.073993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.071574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.060090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.117064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.556905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:34.707907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.760789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.723787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.171547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.017914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.932100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.167123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.157473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.165571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.227862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.639177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:34.806894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.845078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.807492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.238297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.091532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.022633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.249044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.235265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.262521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.311049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.723473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:34.920151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.942038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.891316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.307317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.170326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.116027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.361881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.330486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.403224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.407259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.821138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.011813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.017437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.976307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.377796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.257165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.208306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.464216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.437382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.503927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.777903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.907663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.091371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.098697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:37.649139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.440061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.329046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.290938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.539016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.507590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.591012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.873495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:46.019376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.206920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.195454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:37.730434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.523364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.415605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.378085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.619552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.592494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.676448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.979452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:46.119598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.301583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.282215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:37.800202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.603384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.509439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.724307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.722305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.673157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.765812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.056908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:46.211170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.404260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.371457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:37.870229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.686141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.589622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.811085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.809629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.756902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.852607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.158377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:46.315312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.490789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.452451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:37.946094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.765311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.677891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.896441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.903502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.870983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:43.948553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.269679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:46.409482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:35.587864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:36.548606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.021916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:38.843706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:39.763099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:40.979341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:41.987002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:42.965714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:44.018551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-07T05:55:45.371962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-10-07T05:55:50.332155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
credit.policydays.with.cr.linedelinq.2yrsdtificoinq.last.6mthsinstallmentint.ratelog.annual.incnot.fully.paidpub.recpurposerevol.balrevol.util
credit.policy1.0000.1790.0710.2050.4640.6520.0950.3090.1150.1570.0560.0410.2170.107
days.with.cr.line0.1791.0000.0950.0730.252-0.0420.202-0.1340.4000.0290.1020.0660.324-0.004
delinq.2yrs0.0710.0951.000-0.018-0.2370.021-0.0080.1730.0300.0000.0010.000-0.054-0.032
dti0.2050.073-0.0181.000-0.2140.0280.0630.216-0.0600.0310.0090.1010.3760.334
fico0.4640.252-0.237-0.2141.000-0.1740.085-0.7450.1080.153-0.1480.082-0.095-0.520
inq.last.6mths0.652-0.0420.0210.028-0.1741.000-0.0070.1740.0310.1460.0560.032-0.023-0.016
installment0.0950.202-0.0080.0630.085-0.0071.0000.2430.4310.068-0.0280.1400.3520.096
int.rate0.309-0.1340.1730.216-0.7450.1740.2431.0000.0420.1590.0940.1110.1490.473
log.annual.inc0.1150.4000.030-0.0600.1080.0310.4310.0421.0000.0590.0130.1020.4160.053
not.fully.paid0.1570.0290.0000.0310.1530.1460.0680.1590.0591.0000.0600.0970.0520.082
pub.rec0.0560.1020.0010.009-0.1480.056-0.0280.0940.0130.0601.0000.029-0.0260.071
purpose0.0410.0660.0000.1010.0820.0320.1400.1110.1020.0970.0291.0000.0380.122
revol.bal0.2170.324-0.0540.376-0.095-0.0230.3520.1490.4160.052-0.0260.0381.0000.515
revol.util0.107-0.004-0.0320.334-0.520-0.0160.0960.4730.0530.0820.0710.1220.5151.000

Missing values

2025-10-07T05:55:46.523134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-07T05:55:46.724003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

credit.policypurposeint.rateinstallmentlog.annual.incdtificodays.with.cr.linerevol.balrevol.utilinq.last.6mthsdelinq.2yrspub.recnot.fully.paid
01debt_consolidation0.1189829.1011.35040719.487375639.9583332885452.10000
11credit_card0.1071228.2211.08214314.297072760.0000003362376.70000
21debt_consolidation0.1357366.8610.37349111.636824710.000000351125.61000
31debt_consolidation0.1008162.3411.3504078.107122699.9583333366773.21000
41credit_card0.1426102.9211.29973214.976674066.000000474039.50100
51credit_card0.0788125.1311.90496816.987276120.0416675080751.00000
61debt_consolidation0.1496194.0210.7144184.006673180.041667383976.80011
71all_other0.1114131.2211.00210011.087225116.0000002422068.60001
81home_improvement0.113487.1911.40756517.256823989.0000006990951.11000
91debt_consolidation0.122184.1210.20359210.007072730.041667563023.01000
credit.policypurposeint.rateinstallmentlog.annual.incdtificodays.with.cr.linerevol.balrevol.utilinq.last.6mthsdelinq.2yrspub.recnot.fully.paid
95680all_other0.197937.0610.64542522.176675916.0000002885459.86010
95690home_improvement0.1426823.3412.4292163.627223239.9583333357583.95001
95700all_other0.1671113.6310.64542528.066723210.0416672575963.85001
95710all_other0.1568161.0111.2252438.006777230.000000690929.24011
95720debt_consolidation0.156569.9810.1104727.026628190.041667299939.56001
95730all_other0.1461344.7612.18075510.3967210474.00000021537282.12001
95740all_other0.1253257.7011.1418620.217224380.0000001841.15001
95750debt_consolidation0.107197.8110.59663513.096873450.0416671003682.98001
95760home_improvement0.1600351.5810.81977819.186921800.00000003.25001
95770debt_consolidation0.1392853.4311.26446416.287324740.0000003787957.06001